The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability

Lu ZHOU; Rong-Hua ZHANG

doi:10.1007/s00343-025-4330-y

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The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability

Physics | Updated：2025-12-16

- The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability
- Journal of Oceanology and Limnology Vol. 43, Issue 6, Pages: 1688-1708(2025)
- Affiliations：
  
  1.State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Joint International Research Laboratory of Climate and Environmental Change (ILCEC), Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
  2.State Key Laboratory of Climate System Prediction and Risk Management/School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  3.Laoshan Laboratory, Qingdao 266237, China
- Author bio：
  
  rzhang@nuist.edu.cn
- Funds：
- DOI：10.1007/s00343-025-4330-y
  CLC：
- Received：06 December 2024，
  
  Online First：10 February 2025，
  
  Published：01 November 2025
- Accepted：
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ZHOU Lu,ZHANG Rong-Hua.The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability[J].Journal of Oceanology and Limnology,2025,43(06):1688-1708.
DOI：

ZHOU Lu,ZHANG Rong-Hua.The 3D-Geoformer for ENSO studies: a Transformer-based model with integrated gradient methods for enhanced explainability[J].Journal of Oceanology and Limnology,2025,43(06):1688-1708. DOI： 10.1007/s00343-025-4330-y.

摘要

Abstract

Deep learning (DL) has become a crucial technique for predicting the El Niño-Southern Oscillation (ENSO) and evaluating its predictability. While various DL-based models have been developed for ENSO predictions

many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific. To address this three-dimensional (3D) limitation and represent ENSO-related ocean-atmosphere interactions more accurately

a novel this 3D multivariate prediction model was proposed based on a Transformer architecture

which incorporates a spatiotemporal self-attention mechanism. This model

named 3D-Geoformer

offers several advantages

enabling accurate ENSO predictions up to one and a half years in advance. Furthermore

an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature (SST) variability in the eastern equatorial Pacific. Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events

particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator. By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields

the 3D-Geoformer represents a significant advancement in ENSO prediction. This study provides details in the model formulation

analysis procedures

sensitivity experiments

and illustrative examples

offering practical guidance for the application of the model in ENSO research.

关键词

Keywords

references

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Related Author

Peng LIAN

Le GAO

Peng LIAN

Le GAO

Shuangying DU

Chuan GAO

Rong-Hua ZHANG

Related Institution

Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences

University of Chinese Academy of Sciences

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